Tenure-track Interview - Research Presentation - Dr. Mina Maleki

Fri, 02/09/2018 - 3:00pm - 4:00pm

3105 Lambton Tower

Machine Learning Approaches in Proteomics and Health Science

Healthcare community now believes that diagnosis and treatment of complex diseases such as cancer are likely to emerge not only from pure biomedical research but also from the intersection of biology, mathematics, machine learning, and data analytics. Machine learning, a field of artificial intelligence, has been successfully used for data analysis in many different applications, including text mining, bioinformatics, transportation, finance, and many others. Proteomics and analysis of protein-protein interactions (PPIs) are one of the main applications of machine learning which has a major role in creating a predictive, preventative, and personalized approach to medicine and healthcare. In this talk, a brief introduction to machine learning and its applications in health will be presented. Then, starting from different problem statement in the field of protein-protein interactions, a schematic machine learning model for the prediction and analysis of PPIs types will be discussed. Then, some part of accomplished experiments will be shown following related results and computational and biological analysis. Finally, some of the future direction in the field of machine learning and health science will be presented.

Bio: Mina Maleki received her Bachelor’s degree in Computer Engineering from Azzahra University, Tehran, Iran, in 2002, her Master in Computer Engineering and Information Technology from Amirkabir University of Technology, Tehran, Iran, in 2006, and her Ph.D. in Computer Science from the University of Windsor, in 2014. She is currently working as a sessional instructor at the University of Windsor and a SOSCIP TalentEdge postdoctoral research fellow at the Cross Border Institute (CBI) at the University of Windsor. Her research interests are mainly focused on machine learning and pattern recognition, bioinformatics, proteomics, text and big data mining. Her interest in the field of machine learning developed during her master studies in 2004. Since then, she has been involved in many IT and research projects related to employing machine learning approaches in different areas including text mining, real time locating system, bioinformatics, and transportation. She has published more than 30 publications in journals and conferences in machine learning and bioinformatics.